Results 111 to 120 of about 40,132 (204)
Is A Little Learning Dangerous?
Noûs, EarlyView.ABSTRACT I argue that a little learning is often dangerous even for ideal reasoners who are operating in extremely simple scenarios and know all the relevant facts about how the evidence is generated. More precisely, I show that, on many plausible ways of assigning value to a credence in a hypothesis H, ideal Bayesians should sometimes expect other ...
Bernhard Salow
wiley +1 more source
A Wide Range No-Regret Theorem [PDF]
In a sequential decision problem at any stage a decision maker, based on the history, takes a decision and receives a payoff which depends also on the realized state of nature.
Ehud Lehrer, Dinah Rosenberg
core
Assessing the Effectiveness of Workers' Selection Exams: The Case of the Bank of Italy
Oxford Bulletin of Economics and Statistics, EarlyView.ABSTRACT High‐stakes exams can be used to rank and select candidates for job openings, and the ability of those selected hinges on the design of the exam. I propose a method to model candidates' performance to assess how effective the exam is at selecting high‐ability candidates.
Santiago Pereda‐Fernández
wiley +1 more source
Bayesian Posteriors Without Bayes' Theorem
, 2012 The classical Bayesian posterior arises naturally as the unique solution of several different optimization problems, without the necessity of interpreting data as conditional probabilities and then using Bayes' Theorem.
Theodore P. HILL, DALL'AGLIO, MARCO
core
Personnel Psychology's 40 Questions Series: Artificial Intelligence
Personnel Psychology, EarlyView.ABSTRACT In this article, we present a curated set of 40 questions on Artificial Intelligence (AI) to address its rapidly evolving role in Industrial/Organizational (I/O) Psychology, Human Resources (HR), and Organizational Behavior (OB) research and practice. We solicited questions from our professional networks and organized the responses into themes:
Emily D. Campion, Scott Tonidandel
wiley +1 more source
Bayes’ Theorem: A Model for Human Probability Estimate Revision
, 1967 The purpose of this study was to examine Bayes\u27 Theorem as a model for the description of how humans utilize information based on uncertain (probabilistic) relationships between the relevant cues and the outcome ...
Hickok, William H.
core
Simulations All the Way Up! An Atheist's Response to the Fine‐Tuning Argument
Analytic Philosophy, EarlyView.ABSTRACT So the Fine‐tuning Argument goes, because it is so unlikely for the physical constants of the laws of nature to have taken the values that they in fact take, we should significantly raise our credence that God exists. Simulation Arguments argue that our world might be (or, in stronger versions, that it probably is) a mere computer simulation ...
Nikk Effingham
wiley +1 more source
Diagnosing ectopic pregnancy using the bayes theorem and neural network: a validation of a retrospective cohort study. [PDF]
Rev Bras Ginecol ObstetMaroni L, Silva PC, Kunst R, Savaris RF.
europepmc +1 more source
Sparse Minimum Redundancy Maximum Relevance for Feature Selection
Scandinavian Journal of Statistics, EarlyView.ABSTRACT We propose a feature screening method that integrates both feature–feature and feature–target relationships. Inactive features are identified via a penalized minimum Redundancy Maximum Relevance (mRMR) procedure, which is the continuous version of the classical mRMR penalized by a non‐convex regularizer, and where the parameters estimated as ...
Peter Naylor +3 more
wiley +1 more source